Audience
AI founders, enterprise AI buyers, infra strategists
OCR Provenance runs on the user's hardware, keeps data local, meters usage, and avoids the vendor GPU burden of traditional SaaS.
Proof / alldata.md / 8:59

Audience
AI founders, enterprise AI buyers, infra strategists
Core idea
The vendor does not need to host the workload if the customer's local inference hardware can run the AI and the billing layer is lightweight.
Watch on YouTube· 8:59
This gives frontier-adjacent teams a distribution pattern for sensitive AI tools: local processing, cloud fallback, auditable billing.
Watch videoOpen the full video on YouTubeThe videos are raw build context. These notes translate them into the shortest useful frame for creators, companies, and AI lab readers.
User-provided compute can invert AI SaaS margins.
Sensitive data can stay inside the buyer's environment.
Billing and trust still need careful engineering.
Related notes stay inside the same problem area first, then move to the next useful context.

Watch + read / 12:19
A document pipeline should extract text, images, metadata, entities, relationships, and citations back to source files.

Watch + read / 5:02
The operating posture behind Teleox: treat AI output as unverified until a separate process can trace evidence and failure modes.

Watch + read / 5:31
AI-assisted engineering only scales when the workflow is built around verification, state checks, and zero-trust development.
Send the audience, data type, target task, proof bar, and sharing limits.